Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
| Autor: | Xiaoran Xu, Yan Zhang, Yuan Zhang, Hanning Zhou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Flexibility (engineering)
FOS: Computer and information sciences Computer science business.industry 02 engineering and technology Machine learning computer.software_genre Matrix decomposition Computer Science - Information Retrieval Recommendation model 020204 information systems Path (graph theory) 0202 electrical engineering electronic engineering information engineering Embedding Overhead (computing) 020201 artificial intelligence & image processing Artificial intelligence Differentiable function business computer Information Retrieval (cs.IR) |
| Zdroj: | WSDM |
| Popis: | Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and flexibility. However, they also have such intrinsic limitations as lacking explainability and suffering from data sparsity. In this paper, we propose an end-to-end joint learning framework to get around these limitations without introducing any extra overhead by distilling structured knowledge from a differentiable path-based recommendation model. Through extensive experiments, we show that our proposed framework can achieve state-of-the-art recommendation performance and meanwhile provide interpretable recommendation reasons. Accepted by WSDM'2020 |
| Databáze: | OpenAIRE |
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